Job Summary:

We are looking for an experienced Solution Architect to design and deliver scalable, secure, and future-ready Data & Analytics platforms on Azure. The role will focus on architecting enterprise-grade data solutions supporting reporting, analytics, AI/ML, and GenAI initiatives while ensuring compliance, governance, performance, and cost optimization.

Key Responsibilities:

- Design scalable, secure, and high-performance data platform solutions.

- Architect data ingestion, transformation, storage, governance, and consumption layers.

- Review functional and non-functional design documents and ensure alignment with enterprise standards.

- Collaborate with business, engineering, security, enterprise architecture, and platform teams.

- Ensure data solutions comply with architecture, cybersecurity, regulatory, and governance requirements.

- Drive Databricks vendor engagements and Unity Catalog adoption across projects.

- Support AI/ML, MLOps, RAG, and GenAI architecture use cases on enterprise data platforms.

- Identify technical risks, resolve challenges, and support timely delivery of project milestones.

- Optimize platform performance and cloud cost through continuous improvement.

Mandatory Skills:

- Azure Data Lake / ADLS Gen2

- Databricks and Unity Catalog

- Data Architecture and Data Modeling

- Data Integration and Enterprise Data Platform Design

- SQL for analysis, validation, modeling, and performance optimization

- Data Security, Governance, Metadata Management, and Lineage

Preferred Skills:

- Python and PySpark

- Azure Synapse and Cloud-Native Architecture

- AI/ML Lifecycle and MLOps

- RAG and GenAI Solution Design

- Terraform / Infrastructure as Code

- Agile, DevOps, Cost Optimization, and Stakeholder Management

Required Experience:

- 10+ years of overall experience preferred, with a strong background in data architecture and engineering.

- Proven experience designing and implementing large-scale Azure-based data platforms.

- Strong expertise in Azure Data Lake, Databricks, SQL, data modeling, and integration patterns.

- Experience with metadata management, data governance, secure data access, and architecture governance.

- Ability to translate business requirements into robust, scalable, and cost-effective technical solutions.

- Strong communication skills with experience working with senior business and technology stakeholders.

Good to Have:

- Hands-on experience with AI/ML solution architecture and model lifecycle awareness.

- Knowledge of MLOps, model deployment, monitoring, and operational governance.

- Exposure to RAG, GenAI, Terraform, ARM templates, Python, PySpark, Synapse, Agile, and DevOps